A model for talent identification in cricket based on owa operator

International Journal of Information Technology & Management Information System (IJITMIS),

ISSN 0976 – 6405(Print), ISSN 0976 – 6413(Online) Volume 4, Issue 2, May - August (2013), © IAEME

40

A MODEL FOR TALENT IDENTIFICATION IN CRICKET BASED ON

OWA OPERATOR

1Gulfam Ahamad,

2S. Kazim Naqvi

,

3M.M. Sufyan Beg

1FTK-Centre for Information Technology, Jamia Millia Islamia, New Delhi – 110025, India.

2FTK-Centre for Information Technology, Jamia Millia Islamia, New Delhi – 110025, India. 3Department of Computer Engineering, Jamia Millia Islamia, New Delhi – 110025, India.

ABSTRACT

Talent identification in sports is a challenging and significant task which is considered

highly subjective. However, several attempts have been made in the past to reduce the

subjectivity in this task. In this paper we have reviewed several talent models which have been

proposed in the past. We have also presented a brief summary of each of these models

focusing on their modus-operandi. The paper also identifies essential parameters for talent

assessment in cricket. A model based on Ordered Weighted Averaging Aggregation (OWA)

operator has also been proposed. The paper also presents an example demonstrating the

application of the proposed algorithm on sample data.

Keywords: Talent identification, OWA, Linguistic Quantifier, Talent Classifier.

1.0 INTRODUCTION

The ability to perform well in sports may vary amongst individuals. A person may be

exceptionally good at one sport whiles he may only be average at others. It is interesting to

study what determines the ability of a person to excel or not excel in a given sport. This ability

is commonly referred to as talent. Talent refers to the skills that someone has naturally to do

something that is hard. A number of authors [20], [19], [14] have defined talent as an

increasable natural endowment of a superior quality of a person.

Talent identification is a process to identify the ability of superior quality. It is a

complex multifaceted, multidimensional and multi-stage process [5] [11] [17] [28]. Many

earlier studies [19] [12] [29] have characterized the talent by a number of factors viz. Health,

Motor, Functional, Morphological, Physiological, Anthropometric, Psychological, Social,

Cultural, Game Intelligence, Technical/ Tactical Abilities and Genetics. Although, no

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ISSN 0976 – 6405(Print) ISSN 0976 – 6413(Online)

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consensus seems to have emerged on the completeness of the above parameters which are

believed to contribute towards talent in an individual.

Talent identification is another area with significant importance for individuals and

sports organizations. Correct and timely identification of sports talent can build careers and

bring glory to the nations. On the other side, persistence on incorrectly chosen sports will

invariably lead to wastage of time and resources. To identify talent in sports a number of

studies have been made in the past [8] [18] [29] [1] [21] [28] [9] [6] [6] [5] [26] [27] [26] [3]

[2] [13]. During Our literature survey, we did not come across any study for Talent

Identification in Cricket.

In this paper, we have proposed a model based on OWA Operator to assess the talent

of a cricket enthusiast. In section 1.0 of the paper, we summarize the frequently cited studies

in Talent Identification. The studies summarized in section 1.1 also conclude that no Talent

Identification Model has been developed for Cricket. In section 2.0, we present the parameters

which can be effectively used for building talent identification model for cricket. In section

3.0, we discuss OWA operator. Applications of OWA operator for talent identification in

cricket has been discussed in section 4.0. In Section 5.0, we present the conclusions.

1.1 Talent Identification Models in Sports Timely talent identification in sports is very significant and challenging task. To

identify talent in sports, various computer based talent identification models have been

proposed. These models could potentially play a very significant role in lives of sports

enthusiast and sports organizations. Some of these are listed below [7]:

• An individual can make timely decision to pursue a sport of his interest or not.

• Provide a cost effective way for assessing talent levels without assistance of coaches.

• They can help in increasing the confidence levels in athletes.

• Online talent models can even be more potent as they provide unparalleled reach. They

can be accessed anytime anywhere by anyone.

• Online Talent Identification models can also help sports authorities in getting

indications on talent distribution in various geographical regions within the country.

Such model can help in separating extraordinarily talented athletes from average.

• They can also address the challenge of inadequate number of coaches available in the

country.

All Talent Identification models leverage physical characteristics of athletes which

they deem significant for the sport. Most of the models have applied statistical techniques

including variance [8], standard deviation, t-test, regression [3] [27] [6] [29], ANOVA [9],

MANOVA [26] [4] [21], Few models have also attempted to use Fuzzy Logic and Expert

Systems. Also majority of the models have been developed for Track & Field sports [1] [18]

[8], a few of them have been developed for soccer [21] [24] [28], hockey [4], water polo [6],

baseball [3] [2], handball [26] and table-tennis [13]. Here we present a brief summary of 15 of

such models.

In [8], a model was proposed to enable identification of talent in track and field sports

in Iran. The model chose the age range of 6-12 years. The basic parameters employed by the

model are motor ability, anthropometric, psychological, physiological, sociological and

cultural characteristics of prospective athlete. The model used statistical variance on the

identified parameters.



42

A model for 14 identified sports (athletics sprint jump, martial arts of kicking type,

martial arts of pulling and pushing type, football, tennis, handball, volleyball, water polo,

rowing, swimming, athletics long distance running, basketball, athletic throwing and

gymnastics) was proposed [18]. The model was applied on the athletes in the age range of 6-

18 years. The model uses characteristics which include: motor skills, morphological and

functional and it uses expert system and fuzzy logic membership technique to help rate talent

of a person in a particular sport.

In [29], Model was presented an application for selection of sports talent using

statistical regression equation with computer programming is proposed for athletic jump

without any age criteria. Authors have claimed that their approach can also be applied to other

sports as well.

In [1], a model was proposed for the age range of 11-15 years with the characteristics

of sports interactive task, physiological capacities, motor capacities and biometrics qualities. It

was implemented on Scottish children with the help of statistical techniques. The model was

meant to determine talent of children in 12-identified sports (high jump, long jump, sprinting,

hurdling, karate, triathlon, shot-put, skiing (DH), curling, hockey, tennis and squash)

In [21], a model was proposed for the age range of 15-16 years with anthropometric,

physiological, psychological and soccer specific characteristics. It used Multivariate Analysis

of Variance (MANOVA) technique to distinguish between elite and sub-elite groups on the

basis of performance on test items.

In [28], a model was proposed to determine the relationship between physical and

performance characteristics for the age of 13-16 years. The authors applied MANCOVA

technique to the data to identify the most important physical parameters for soccer.

In [9], a model predicted talent of players in age range of 18-years. The model used

ANOVA statistical methods on the physical characteristics of player which include body

height, weight, skeletal age, choice reaction time, stepping speed and stepping endurance.

A study [6] was meant to identify and develop talent in water polo sports. It was

proposed for the age range of 14-15 years with the characteristics of motor ability related to

water activity, physical ability and evaluation of game intelligence. The authors applied

statistical analysis unpaired t-test ANOVA on the data to assess the talent.

This research [5] demonstrated application of MANCOVA to understand the

relationship between multidimensional performance characteristics and level of performance

in talented youth hockey players. The model was implemented for the age range of 13.2-14.2

years and considered the anthropometric, physiological, psychological, technical and tactical

characteristics.

In the [16], a model was meant to detect talent in handball sports. The model used

MANCOVA on morphological, physical fitness, anthropometric, hand ball specific motor

skills and maturation characteristics. The model was applied on group of 14-16 years.

In [27] a model used essential characteristics of team sport. The model applied regression and

canonical analysis techniques.

A method in [28] is proposed for selecting players in team sports game with the help of

standard deviation and expert system.

In [3], a model was proposed for Croatia. The model uses characteristics which include

potential, morphological, technical knowledge and coordination abilities with the help of

statistical techniques t-test, z-test, and correlation.

This was proposed for different age groups with the help of graphical statistical

techniques. The model used characteristics which included special tactical and technical skills

for basketball [2].



43

In [13], a model was developed for children in the age range of 6-8 years with the help

of t-test with standard deviation. The model uses characteristics including height, weight, skin

fold, deep bend for flexibility, polygon for coordination, bent arm hang on horizontal bar for

strength test, sit up test for trunk strength, standing jump for explosive strength, 60 meters

sprint for speed and 600 meters run for endurance.

Based on our survey of published literature we can infer that no model for identifying of talent

in cricket has been reported as on date. Further, to develop TID model for cricket we need to

identify parameters which can be measured and thus can produce data for analysis. In the next

section we identify such parameters which may play important role in identifying talent in

Cricket.

2.0 PARAMETERS FOR TALENT IDENTIFICATION IN CRICKET

The Talent Identification report [25] summarizes the talent parameters for cricket. The

parameters are based on physical/motor, anthropometric and cognitive characteristics. To

quantify these parameters various tests have been identified in [7].The identified

characteristics and its parameters are listed in Table1 and Parameters with tests are listed in

Table2.

Talent Characteristics Talent Parameter

Physical/ Motor Ability

Tests

Speed, Agility, Flexibility, Balance

Static/ Dynamic, Endurance, Upper

Body Strength, Lower Body

Strength, Fatigue Index, Shoulder

Flexibility, Bowler Accuracy, Under

Arm Throw Accuracy, Under Arm

Throw Accuracy, Catching Ability,

Ground Fielding Ability

Cognitive Ability Tests Self Motivation, Reaction Time,

Hand Eye Coordination, Creativity,

Decision Making, Self Control &

Self Monitoring, Integrity and Work

Ethic, Willingness, Concentration

and Focus, Stress

Anthropometric Tests Body Mass Index, Vo2max

Table 1: Talent requirements for cricket in terms of various parameters with characteristics



44

Parameters Tests

Speed Speed Test (T1)

Agility Illions Agility Test (T2)

Endurance Step Up and Down Test (T3)

Stress Stress Test Quiz (T4)

Self Motivation Self Motivation Test Quiz (T5)

Upper Body Strength Push Up Test (T6)

Lower Body Power Hop Run Test (T7)

Reaction Ruler Catching Test (T8)

Flexibility Sit and Reach Test (T9)

Fatigue Index RAST (Running Based Anaerobic Sprit) Test (T10)

Bowler Accuracy Bowler Accuracy Test (T11)

Through Catching Accuracy Through Catching Accuracy Test (T12)

Under Arm Through

Accuracy

Under Arm Through Accuracy Test (T13)

Catching Ability Catching Ability Test (T14)

Ground Fielding Assessment of Clean Pick Ups. (T15)

VO2 Max Maximum Oxygen Up Taken Test (T16)

Body Mass Index Weight/ Height2

(T17)

Hand Eye Coordination Catching and Throwing the Ball in Cyclic Order with

Hands (T18)

Creativity Creativity Test Quiz (T19)

Decision Making Decision Making Ability Test Quiz (T20)

Self Control and Self

Monitoring Self Control and Self Monitoring Test Quiz (T21)

Will Power Will Power Test Quiz (T22)

Self Confidence Self Confidence Test Quiz (T23)

Integrity and Work Ethic Integrity and Work Ethic Ability Test Quiz (T24)

Shoulder Flexibility Shoulder Flexibility Test Based On Physical Action (T25)

Balance Beam Test for Balance (T26)

Balance in Static Form Balance Test Based on Physical Action (T27)

Concentration and Focus Concentration and Focus Skill Test Quiz (T28)

Table 2: Talent requirements for cricket in terms of various tests and parameters.

Various quantitative tests (given in table1) have been identified in [7] which can help

in measuring each one of the above characteristics. Given the ability to quantify the

characteristics for talent assessment in Cricket, a talent identification model can be build. In

section 4.0 we first introduce Ordered Weighted Averaging/Aggregation operator, which we

apply to the problem of Talent Identification in section 4.0.



45

3.0 ORDERED WEIGHTED AVERAGING AGGREGATION (OWA) OPERATOR

The OWA operator was introduced by [22] to provide a means of aggregation, which

unifies in one operator the conjunctive and disjunctive behavior. It provides a parameterized

family of aggregation operators including many of the well-known operators like maximum,

minimum, k-order statistics, median and arithmetic mean. For n different scores nxxx ,...,, 21 ,

the aggregation of these scores may be done using the OWA operator as follows.

OWA ( nxxx ,...,, 21 ) = ∑=

n

i

ii yw1

where iy is the ith

largest score from amongst nxxx ...,, 21 . The weights are all non-negative

iwi,∀ ≥0, and

=∑

=

11

n

i

iw . We note that the arithmetic mean function may be obtained using

the OWA operator, if ∀i, n

wi

1= . Similarly, the OWA operator would yield the maximum

function with iw =1 and iw =0 for all i≠ 1. The minimum function may be obtained from the

OWA operator when nw =1 and iw = 0 for all i ≠ n.

In fact, it has been shown [22] that the aggregation done by the OWA operator is always

between the maximum and minimum. To find the values of the weights iw , we need to make

use of the relative fuzzy linguistic quantifiers, explained as follows.

3.1 Relative Fuzzy Linguistic Quantifier

A relative quantifier, Q: [0, 1] → [0, 1], satisfies:

Q(0) = 0,

[ ]1,0∈∃r such that Q(r) = 1.

In addition, it is non-decreasing if it has the following property:

[ ]1,0, ∈∀ ba , if a> b, then Q(a) ≥ Q(b).

The membership function of a relative quantifier can be represented as shown in [1]:

−

−=

1

0

)(ab

arrQ

if

if

if

br

bra

ar

>

≤≤

<

, …………..[1]

where [ ]1,0,, ∈rba and )/()( miQrQ =

[22] Computes the weights wi of the OWA aggregation from the function Q describing the

quantifier. In the case of relative quantifier, with m criteria [23],

,,....,2,1)),/)1(()/( mimiQmiQwi =−−= with Q(0) = 0.



46

3.2 Normalized Adequacy Coefficient The adequacy coefficient [15] is an index used for calculating the differences between

two elements, or two sets, or two fuzzy sets, etc. The adequacy coefficient is very similar to

the hamming’s distance but with some differences. It makes it more complete in a lot of

decision making problems, especially, when we cannot accept that one set (Y) is higher than

the other (X). The similarity between two sets in the adequacy coefficient is calculated with

( ))1(1 yx +−∧ or the complement ( ))(0 yx −∨ . For two sets },...,{ 1 nxxX = and

},...,{ 1 nyyY = , the weighted adequacy coefficient can be defined as follows.

Definition: According to [15], A weighted adequacy coefficient of dimension-n is a mapping

WAC: [ ] [ ] [ ]1,01,01,0 →×nn

that has an associated weighting vector W of dimension n with

∑=

=n

j

jw1

1 and [ ]1,0∈jw , such that:

WAC [ ])1(1),...,,(1

,11 ii

n

i

inn yxwyxyx +−∧=∑=

,

where ix and iy are the ith

arguments of the sets X and Y, respectively.

Note that if nwi /1= , for all i, then, the weighted adequacy coefficient becomes the

normalized adequacy coefficient (NAC).

NAC [ ])1(11

),...,,(1

,11 ii

n

i

nn yxn

yxyx +−∧= ∑=

,

4.0 APPLICATION OF OWA TO TALENT IDENTIFICATION PROBLEM IN

CRICKET

As discussed in section 2, the talent identification is a difficult task. Attempts have

been made to identify parameters to assess talent in Cricket. These parameters were

summarized in section 3. To build a cricket talent assessment model, we collected responses

from cricket experts for the identified 28-tests. The experts were requested to categorize the

result range of each of the 28-tests to judge the suitability of the result value for each of the

five talent classes, viz. Extraordinary Talented [EOT], Very Much Talented [VMT], Much

Talented [MT], Moderately Talented [MDT], and Not Talented [NT]. A sample question to

record assessment of an expert is shown in figure1:

Please fill your rating for the "Speed Test" for the five categories of talent classes.

Run 35 meters in a straight line and record the time in seconds.

[<4.80 sec ] [4.80- 5.09 sec] [5.10-5.29sec] [5.30- 5.60 sec ] [ >5.60 sec ]

Extraordinary Talented

Very Much Talented

Much Talented

Moderately Talented

Not Talented

Figure1: A sample question from questionnaire.



47

It may be observed that expert is expected to record his assessment for each of the 5-

talent classes by choosing an interval. For each of the chosen interval we record its average

value as the assessment vet[i] of the expert. All such assessments for each talent class-i are

recorded in an Expert Assessment ][iEA matrix as follows:

2821 ... ttt

][iEA =

ne

e

e

.

.

.

2

1

][...][][

......

......

......

][...][][

][...][][

21

22212

12111

iviviv

iviviv

iviviv

mnnn

m

m

tetete

tetete

tetete

………. (2)

.281, ≤≤∀ jj jt is a test aimed at quantifying the physical ability.

where [ ]4,3,2,1,0∈i

4

3

2

1

0

=

=

=

=

=

i

i

i

i

i

for

for

for

for

for

NT

MDT

MT

VMT

EOT

Test Definition: Each test it is a 5x5 matrix comprising of rows representing Talent Classes

(TC) and columns representing permissible responses. Each response is recorded in form of

(a, b) where a is the lowest permissible value and b is the highest permissible value for each

class and each test. Thus,

].28,1[)Re,( ∈∀= isponseTCTCRti

Where, TCR means Talent Class Response Matrix.

The data for all the values obtained in above 5-matrices [eqn. 2] were normalized using min-

max transformation so that each value now falls in [0, 1]. Min-max normalization maps a

value v of an attribute A to v′ in the range of [new_minA, new_maxA] by computing.

( ) AnewAnewAnewAA

Avv min_min_max_

minmax

min' +−

−

−= .

The five normalized matrices ][iEA now comprise the Knowledge Base for our Model. A

broad architecture for the Talent Identification Model is shown in figure 2. Any cricket



48

enthusiast can provide the outcome result values for himself for each of the 28-tests. The

model then uses the Talent Classifier described in section 4.2 to classify the talent of person in

any one of the five possible talent classes.

.

Figure 2: Architecture of Cricket Talent Identification Model

4.1 Algorithm for Talent Identification The algorithm for talent identification is given below:

Algorithm to compute weights (wi)

begin

for each talent class ]4,0[];[ ∈jjTC .

for each test ]28,1[, ∈iti

for each expert ],0[);( nkkE ∈ where n = total no. of experts

;/)( nkkr =

for each expert ],0[);( nkkE ∈

begin

if akr <)(

0)( =kQR

else if )({ kra ≤ }b≤

)/())(()( abakrkQR −−=

else

})( bkr >

Talent

Classifier (TC)

Normative Data for Talent (NDFT)

1o

2o . . . . . io . . . . . .

28o

Knowledge Base

EOT

VMT

MT

MDT

NT

Cricket Enthusiast



49

1)( =kQR

end;

if )0( <>k

);1()(],[ −−= kQRkQRikw

End Loop k;

End Loop j;

End Loop i;

//For the Talent Class ]4,0[];[ ∈jjTC and test ]28,1[, ∈iti calculate OWA [j,i] as follows:

for each talent class ]4,0[];[ ∈jjTC .

for each test ]28,1[, ∈iti for each expert ],1[);( nkkE ∈ where n = total no. of experts

if a < b then

);]),[(d_assign(arrange_an = y(k) ascendingjEA

itΠ

else

);]),[(d_assign(arrange_an = y(k) descendingjEA

itΠ

end for k;

sum: = 0;

for each expert ],1[);( nkkE ∈ where n = total no. of experts

sum += w(k,i)*y(k);

OWA(j, i) = sum;

End for j;

End for i;

End;

4.2 Classification of Talent

To classify the talent of a person, we first record his/ her test records, oi for each of the

28-tests in a 28x1 NDTF (Normative Data for Talent) matrix as follows:

)............,()( 2821 ooovNDFT =

We now calculate the normalized adequacy coefficient (NAC) between NDFT and OWA(j,i)

where 4..0∈j and 28..1∈i . The classifier now classifies the talent based on the

28..1,4..0))),,(,(( ∈∈ ijijOWANDFTNACMax . For example, if the maximum value is

obtained for j=3, then Talent class is identified as “Much Talented”.

4.3 Experiments and Results We now demonstrate the application of algorithm on the i

th test viz. the speed test and

the jth

talent class, viz. “Extra Ordinary Talent (EOT)”. Firstly, we normalize the rough range

for the identified test for the five talent classes. The sample normalized values are shown in

figure 3. We assume that assessments of 33 experts (E1, E2, E3,….., E33) are available and

recorded in the matrix Experts Assessment (EA) as described in equation 2.



50

Figure3: Relative Fuzzy Linguistic Quantifiers

For the jth

talent class, with the sample normalized interval a= 0, b= 0.465 we now

calculate weights using algorithm described in section 4.1. This is also depicted in table 3

below:

Table3: Calculation of Weights

i= 0,1,2,…..33 0 1 2 … 33

i/33 = r 0/33= 0 1/33

=0.03

2/33

=0.06

… 33/33

=1

)()/( rQniQ =

−

−=

1

0

)(ab

arrQ

if

if

if

br

bra

ar

>

≤≤

<

0

0.06

0.13

…

1

)1

()(n

iQ

n

iQwi

−−=

Not

Required 0.06-0

= 0.06

0.13-0.06

=0.07

… 1-1

=0.000

0

1

1

EOT VMT MT MDT NT

0.0-0.465 0.46-0.515 0.52-0.549 0.55-0.60 0.601-1.0 x



51

Table4: Weights for Extra Ordinary talented (EOT)

Table5: Experts opinion for Extra Ordinary talented (EOT)



52

Now the ordered weighted averaging aggregation (OWA) operator is used to aggregate

the experts opinions for EOT class.

OWA(0,1) = ∑=

33

1i

[0.06, 0.07, 0.06,……….. 0.00 ]. Ascending or Descending [15] [0.23, 0.22,

0.33, ….… , 0.23) = 0.89.

Similarly, values are aggregated for all the tests. These values are depicted in Table 6.

The normative data for talent (NDFT) of cricket enthusiast for all tests is depicted in Table7.

Now we find the Normalized Adequacy Coefficients using the equation (3) for NDFT of

cricket enthusiast with all five talent class.

Table6: Aggregated values of all talent classes for 28 tests

Table7: Assessed results of cricket enthusiast against 28 tests

Normalized Adequacy Coefficient value for Extraordinary Talent (EOT):

NAC [ ] )3....(..........)1(11

),...,,(1

,11 ii

n

i

nn yxn

yxyx +−∧= ∑=

)].85.09.01(1............)54.023.01(1)489.0219.01(1[28

1

)85.0,9.0,......,28.0,041.0,54.0,23.0,489.0,219.0()(

+−∧+++−∧++−∧=

=− NACEnthusiastVMTNAC

8860714.0]81.24[28

1

]95.0............11[28

1

]95.01.........31.1127.11[28

1

==

+++=

∧++∧+∧

Similarly, the values for other classes are calculated and shown below.

NAC(VMT-Enthusiast)= 0.9209286

NAC(MT-Enthusiast)= 0.8971786

NAC(MDT-Enthusiast)= 0.8983571

NAC(NT-Enthusiast)= 0.8206071



53

Now, Max Operator is used to find the maximum value among the above given normalized

adequacy coefficients.

MAX (NAC(EOT-Enthusiast), NAC(VMT-Enthusiast), NAC(MT-Enthusiast), NAC(MDT-

Enthusiast), NAC(NT-Enthusiast))

= MAX(0.8860714, 0.9209286,0.8971786, 0.8983571, 0.8206071) = 0.9209286

Since the maximum Max(NAC(TC[j], NDFT)) ∀ j ∈ (0,4) is achieved against VMT. We

identify the talent of enthusiast as “Very Much talented”.

5.0 CONCLUSION

This paper reviewed several talent identification models in sports which have been

proposed in research papers. The study revealed that no talent identification model has yet

been proposed for cricket. To develop such a model we identified 28-parameters and

corresponding tests to quantify the talent of an enthusiast against these parameters. We then

build a database of experts’ opinion for classification of talent into five categories based on the

results of the identified tests. An algorithm using Ordered Weighted Averaging Aggregation

(OWA) operator was proposed for aggregation of opinions whose results can be easily used

for classification of talent in cricket. The paper also presents an experiment with results to

demonstrate the application of the model.

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